Modern modems have the ability to automatically adapt the signaling rate based on changing channel conditions. Some channels such as the low voltage power grid have an exceptionally diverse and complex range of impairments that can cripple communication. Conventional methods of adaption require the transmission of known data patterns so that the receiver can compare the received, and presumably impaired, constellation to that of an ideal a priori signal. Such methods waste precious bandwidth and cause latency as they allow only adaption-specific information to be transmitted during the initial learning process. Other adaption methods try to characterize incoming packets by measuring general parameters that affect reception, such as the signal to noise ratio or phase-amplitude deviations of the demodulated constellation. While these adaption methods allow for non-adaption-specific information to be transmitted during the initial adaption process, they often lead to poor modulation mode choices due to the poor estimates provided by performance predictions that are based on general parameters. This is especially true in scenarios where non-Gaussian and non-linear noise are present in the communication channel or medium, such as the case of a low voltage power grid.